Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment

TL;DR

This paper introduces JobShopLib, a modular library for customizing and training graph neural network-based dispatchers for the NP-hard job shop scheduling problem, facilitating future research and achieving near state-of-the-art results.

Contribution

The paper presents JobShopLib, a flexible, modular reinforcement learning environment for training GNN-based dispatchers, enabling easier experimentation and customization in job shop scheduling.

Findings

A GNN model achieved near state-of-the-art results on large-scale problems.

Customization of node features significantly impacts model performance.

The library simplifies experimentation with different scheduling strategies.

Abstract

The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work has attempted to replace these with deep learning models, particularly graph neural networks (GNNs), that learn to assign priorities from data. However, training such models requires customizing numerous factors: graph representation, node features, action space, and reward functions. The lack of modular libraries for experimentation makes this research time-consuming. This work introduces JobShopLib, a modular library that allows customizing these factors and creating new components with its reinforcement learning environment. We trained several dispatchers through imitation learning to demonstrate the environment's utility. One model outperformed various graph-based dispatchers using only individual operation features, highlighting the importance of feature customization. Our GNN model achieved near state-of-the-art results on large-scale problems. These results suggest significant room for improvement in developing such models. JobShopLib provides the necessary tools for future experimentation.